A Framework for Breast Cancer Prediction Using Support Vector Machines

Breast cancer is one of the most widespread types of cancers that usually appear in women worldwide, and early diagnosis is of great importance to survival improvement. As the breast cancer prediction study trial, we are looking forward to working with support vector machines (SVM), a strong machine learning algorithm that is well-known for its good performance in classification tasks. This architecture integrates the features extracted from the data of medical imaging, such as mammograms or MRI, with the data of patients, including patient demographics, family history, and previous health records. We depict the steps of data pre-processing, feature extraction, and model training, and we conduct its evaluation by utilizing the correct performance metrics. Alongside that, we delve into the possible methods for selecting features and optimizing parameters aiming at the improvement of the accuracy of the SVM model. A number of experiments are conducted on a unified dataset of the proposed method demonstrability of accurate diagnosis of the breast cancer presence. This gives healthcare professionals preoperative decision-making support.